{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10.5 Lab -2 : Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:14:29.405094Z",
     "start_time": "2023-09-19T12:14:29.162865Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "import scipy.cluster.hierarchy as shc\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10.5.1K-Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:14:29.412139Z",
     "start_time": "2023-09-19T12:14:29.408191Z"
    }
   },
   "outputs": [],
   "source": [
    "# set seed \n",
    "np.random.seed(2)\n",
    "X = np.random.normal(size = (50,2))\n",
    "X[:25,0] += 3 \n",
    "X[:25,1] -= 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:14:29.624146Z",
     "start_time": "2023-09-19T12:14:29.410720Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.collections.PathCollection at 0x135ab7d00>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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AAEYQOgAAgBGEDgAAYAShAwAAGEHoAAAARhA6AACAEYQOAABghGfPXgHc1tmV0M4jcTW2diiWn6MJpQXKzgrQWdQAYBihA0iitq5RS2sP6Fhrx+nbivNzNK9qrKrGxVwcGQD4F5dXEEidXQltq2/Rhr3HtK2+RZ1d9o9yrK1r1MJ1e84KHJJ0rLVDC9ftUW1do9PDBYBQYKYDgTOQWYrOroSW1h5IeZ/nNx7U1LIiLrUAQJqY6UCgDHSWYueReI+/213D8XbtPBIf8FgBIGwIHQgMu7MUqS61NPYRONK9HwDgXwgdCAwnZili+Tm2nsvu/QAA/0LoQGA4MUsxobRAxX0EipJoriaUFqQ1NgAAoQMB4sQsRXZWRPOqxqb8+3OnlbGIFAD6gdCBwHBqlqJqXExLZpX3eKySaK6WzCqnTwcA9BNbZhEY1izFwnV7er2P3VmKqnExTS0roiMpADiI0IFAsWYpuvfpKInmau60srRmKbKzIpo0pjADowSAcCJ0IHCYpQAAbyJ0IJCYpQAA72EhKQAAMIKZDgBAaHV2JbgUaxChAwAQSgM5HBL9w+UVAEDoDPRwSPQPoQMAYFtnV0Lb6lu0Ye8xbatvSXmAolc5cTgk+ofLKwAAW4JyOSKdwyHZBecsZjoAAH0K0uUIJw6HRP8QOgAAKQXtcoQTh0OifwgdAICU0rkc4QdOHQ6J9BE6AB8KwmI++EfQLkdYh0OmYvdwSKSHhaSAzwRlMR/8I4iXI5w8HBL2EToAH7EW83VnLeZbMqucX5ZwnHU5ItUlFj9ejuBwSPO4vAL4RNAW88E/gnw5wjoc8rpLijVpTKEva/ATQgfgE0FbzAd/sS5HdF+AWRLNZYYNtnF5BfCJoC3mg/9wOQIDRegAfCKIi/ngP9blCKA/uLwC+AS9BQD4naOh4/Dhw3rooYc0efJkVVZWqqamRvX19U4+BRBaXljMR38QAAPh6OWVBx98UJdeeqlqa2uVSCT07LPPqqamRu+++66TTwOElpu9BegPAmCgHAsd8XhcsVhMjzzyiIYMGSJJqq6u1o033qh4PK6CAqZ8ASe4sZiP/iAAnJBW6Ghra1NDQ0PSn40YMUIrV64867YNGzaotLS0X4EjEsLF0FbN1B4u/al9UHZEFecXZmQ83dntD/LvY4v6FXx47ak9bIJYu91a0godu3btUnV1ddKfLV++XNOnTz/959WrV+vVV1/VSy+9lM5TnFZUFO3X3wsCag8nr9a+6WCTrf4g//f495pSVtTv5/Fq/SZQeziFsfa0QkdlZaX27duX8j4dHR1avHix3n//fb388suaPHlyvwbW1HRciZCtUYtETr0Jqd3t0Zjl9doPHm2xfb9xBelv1/V6/ZlE7dQelNqtmvri6ELS5uZmPfDAA+ro6NCaNWs0ZsyYfj9WIqHAvBjpona3R+EOr9YeG2qzP8jQnAGN36v1m0Dtbo/CHWGs3bEts99//73uu+8+5efna/Xq1QMKHAC8g/4gAJzi2EzHxo0btXv3buXm5mrKlCln/ey9997T6NGjnXoqAAZZ/UGS7V6x+PWwLwBmORY6rr322j7XewDwJzf7gwAIDs5eAWALh30BGChCBwDbOOwLwEAQOgB4VmdXgpkVIEAIHQA8ibNegODhaHsAnmOd9dK9E6p11kttXaNLIwMwEIQOAJ5i96yXzq6QdVUCAoDQAcBTdh6J2zrrZXt9i5kBAXAMoQNARnR2JbStvkUb9h7TtvoW2zMTjX0EDsvj6/dymQXwGRaSAnDcQBaBxvpouW75tu0HLVy3R0tmlbOwFPAJZjoAOGqgi0DtnPVyJtZ3AP5B6ADgGCcWgVpnvdjVcLxdO4/Ebd8fgHsIHQAcY3cRaF8hwTrrZVhutq3ntbsOBIC7CB0AHGP3w9/O/arGxbR4Zrmtx7O7DgSAuwgdABxj98Pf7v0mjSnsc31HSTRXE0oLbD0eAHcROgA4xs4i0HRCgp31HXOnlXEeC+AThA4AjslESLDWd3QPMyXRXLbLAj5Dnw4AjrJCQvc+HSXRXM2dVtavkFA1LqapZUWcOAv4HKEDgOMyERKysyKaNKbQuUECMI7QASAjCAkAumNNBwAAMIKZDgBAoHR2JVj/41GEDgBAYAzksEFkHpdXAACBMNDDBpF5hA4AgO85cdggMo/QAQDwPacOG0RmEToAAL7n5GGDyBxCBwDA95w+bBCZQegAAPie04cNIjMIHQAA3+NEYn8gdAAAjOnsSmhbfYs+2HtMmw42ObqbhBOJvY/mYAAAI0w07uJEYm8jdAAAMs5q3NWd1bjLyZkIDhv0Li6vAAAyisZdsBA6AAAZReMuWAgdAICMonEXLIQOAEBG0bgLFkIHACCjaNwFC6EDAJBRNO6ChdABAMg4GndBcrhPx969e7V48WLt3r1bgwYN0tVXX60nnnhC5557rpNPAwDwobMad53oUNnoQl0YPUdZEWY4wsKxmY6Ojg7df//9qqys1ObNm/XRRx/p66+/1u9//3unngIA4HNW467rLynWlLIiLqmEjGMzHTk5Ofrwww+Vl5enrKwsxeNxnTx5UsOHD3fqKQAAgI+lFTra2trU0NCQ9GcjRozQkCFDJEm33367duzYobFjx+ree+/t18DCONtm1Uzt4RLm2qVw10/t1B4UdmuJJBIJ231nN2/erOrq6qQ/W758uaZPny7pVDhpb2/XU089pQMHDujtt99Wdna23acBAAABlFboSFdTU5OuuOIKrV27VuXl5Wn+3ePK3Mi8KRKRioqi1E7toRLm+qmd2oNSu1VTXxxb03H48GFVV1frzTffVHFxsaRTi0slqaAg/YYviYQC82Kki9rdHoU7wly7FO76qd3tUbgjjLU7tnultLRUhYWFWrx4sU6cOKHm5mYtWrRIV199tUpLS516GgAA4FOOhY5IJKIVK1bohx9+UFVVlW688UaNGjVKzz//vFNPAQAAfMzR5mAjR47Uiy++6ORDAgCAgHA0dAAAgqezK3Gqi2hrh2L5OZpQWkBTL/QLoQMA0KvaukYtrT2gY60dp28rzs/RvKqxnJeCtHHgGwAgqdq6Ri1ct+eswCFJx1o7tHDdHtXWNbo0MvgVoQMA0ENnV0JLaw+kvM/zGw+qsytkez4xIIQOAEAPO4/Ee8xwdNdwvF07j8QNjQhBQOgAAPTQ2EfgSPd+gEToAAAkEcvPcfR+gEToAAAkMaG0QMV9BIqSaK4mlKZ/zAXCi9ABAOghOyuieVVjU95n7rQy+nUgLYQOAEBSVeNiWjKrvMeMR0k0V0tmldOnA2mjORgAoFdV42KaWlZER1I4gtABAEgpOyuiSWMK3R4GAoDQAQAwyjrLpf3wt8rt6mLmJEQIHQAAYzjLJdxYSAoAMIKzXEDoAABkHGe5QCJ0AAAM4CwXSIQOAIABnOUCidABADCAs1wgEToAAAZwlgskQgcAwADOcoFE6AAAGMJZLqA5GADAmDPPcmnPyqIjacgQOgAARmVnRVRxfqFisagaG48rQWuO0ODyCgAAMILQAQAAjCB0AAAAIwgdAADACEIHAAAwgtABAACMIHQAAAAjCB0AAMAIQgcAADCCjqQATuvsSmjnkbgaWzsUy8+hPTUARxE6AEiSausatbT2gI61dpy+rTg/R/OqxnIQl8sIgwgKQgcA1dY1auG6PT1uP9baoYXr9nACqIsIgwgS1nQAIdfZldDS2gMp7/P8xoPq7OJULtOsMHhm4JD+FQZr6xpdGllwdXYltK2+RRv2HtO2+hbe9w5jpgMIuZ1H4j0+1LprON6unUfimjSm0MygYDsMTi0r4lKLQ5hVyjxmOoCQa+wjcKR7PzjDbhj889//H9/IHcCskhkZCx0LFizQXXfdlamHB+CQWH6Oo/eDM+yGvFc31+vX//2JZv2vzXww9hOXGM3JSOhYs2aN1q9fn4mHBuCwCaUFKu4jUJREczWhtMDQiCClH/L4Rt5/6VxixMA4HjoOHDigFStW6LbbbnP6oQFkQHZWRPOqxqa8z9xpZawbMMxOGExmIN/Iw7qIkkuM5qS1kLStrU0NDQ1JfzZixAhlZWVpzpw5evLJJ/XJJ5/oiy++6PfAIiH8/WbVTO3h4oXar7kopv+aVa4/dFtEVxLN1bxpZaq6KHOL6LxQv1tS1T4oO6L5VWP1WJKtzKlY38grzi9M6+/V7m/s8foX5+doftXYjLz+Xnrd07nE6MR4vVS7U+zWklbo2LVrl6qrq5P+bPny5aqtrdWVV16pqVOn6pNPPknnoXsoKooO6O/7GbWHk9u1/zwW1c8mX6AtXzTr2PE2FUfzdPm/DTc2w+F2/W7qrfafx6IaNixPi97do6/ibbYfrz0rS7GY/X/PDz77SgvX7VH3eY2vf7xk89IvLtP1l46y/Xjp8MLrPmN4vkZt2K9/xtt6/BtIUkTSyII8zZhwnqP/P3ihdtPSCh2VlZXat29f0p+tW7dOn3/+ud58801HBtbUdFyJcMzsnRaJnHoTUrvbozHLa7WPK8jRuIJT3/y+aW7N+PN5rX6T7NReMTJfb9/7U+04HNc/Dn2jlf+nvs/Hze3qUmPjcVtj6OxK6D/f/izph61125Pv7NbE4qGOfuB67XWfM/XCXmeVEj/+3Kn/H7xWuxOsmvriWJ+Od955R1988YWuuOIKSVJ7e7s6OztVUVGhdevWafTo0Wk9XiKhwLwY6aJ2t0fhjjDXLoW7/r5qz4pENGlMoSaUFujdzxpSLnq0Fv3a/bfccdjeIsodhzPTp8Urr/u0cTEtmVXeo09HSTRXc6eVadq4mOPj9ErtJjkWOlauXHnWn1988UVt2bJFf/nLX5x6CgDIKC+dcdLbWOZVjU3ast6S7qJfFlH+S9W4mKaWFXnmPRBEdCQFAHmrG2VfY0n1jTzdsdKn5WzZWRE672ZQxkLHb37zm0w9NAA4yksH3tkdi1PfyK2tuXYu2QADRRt0AKHmpW6U6YzF+kZ+3SXFmjSmsN+XAOjTApMIHQBCzUvdKN0ai3XJpnszspJorhbPvEQFeYNC1zAMmcGaDgCh5qWFlG6OJdklm2+++15/3HjQE+tcEAzMdAAINS8tpHR7LGdesom3/aDfrt/LqatwFKEDQKh56cA7r4zFS+tcECyEDgCh5qWFlF4Zi5fWuSBYCB0AQi/VQkqT22W9MhYvrXOBM7xygjALSQFA3upG6fZY3F5bAmd5qfEdoQMAfuSlbpRujoWGYcHhpcZ3EpdXAADdeGVtCQbGiwuCCR0AgB68sLYEA+PFBcFcXgEAJOX22hIMjBcXBBM6AAC98tI6F6THiwuCubwCAEAAeaXZ3JkIHQAABJAXFwQTOgAACCivLQhmTQcAAAHmpQXBhA4AAALOKwuCubwCAACMYKYDABBKnV0JT1xyCBNCBwAgdLx0CFqYcHkFABAq1iFo3VuEW4eg1dY1ujSy4CN0AABCw4uHoIUJoQMAEBpePAQtTAgdAIDQ8OIhaGFC6AAAhIYXD0ELE0IHACA0vHgIWpgQOgAAoeHFQ9DChNABAAgVrx2CFiY0BwMAhI6XDkELE0IHACCUvHIIWpgQOgD4CudlYKB4D7mH0AHANzgvAwPFe8hdLCQF4Aucl4GB4j3kPkIHAM8LwnkZnV0Jbatv0Ya9x7StvsXTYw2iILyHgoDLKwA8L53zMry4MLC3Kf35VWP181jUxZGFh9/fQ0FB6ADgeX4+L8Oa0u/uWGuHHlu3R8OG5aliZL4LIwsXP7+HgoTLKwA8z6/nZdiZ0l/07h6m9A3w63soaAgdADzPr+dl2JnS/yreph2HOUY9Hf1ZH+PX91DQOHp5ZdeuXZo9e7YGDx58+rby8nK98cYbTj4NgJCxzstIdpnC4sXzMmxP6Z9gSt+u/m559et7KGgcnen49NNP9dOf/lQ7duw4/R+BA4AT/Hhehu0p/aFM6dsx0C2vfnwPBY2jMx2ffvqpLr30UicfEgBO89t5GdaUfqpLLKMK8jTxPKb0+2J3y+vUsqKU7we/vYeCJq3Q0dbWpoaGhqQ/GzFihD799FPFYjFde+21am1t1eWXX67HH39cI0eOTHtgkRC+/lbN1B4uYa5dSr/+QdkRVZxfmLHxOGlQdkTzq8bqsV6m9COSnpxZrkHZESVCtpY03dc9nS2vfb0/3H4PBfH/edv//6bzoLt27VJ1dXXSny1btkzFxcW64oordMcdd+j777/XM888o1/+8pdau3atsrOz03kqFRWFd+86tYdTmGuXglv/z2NRDRuWp0Xv7tFX8bbTt48qyNOTM8t1/aWjXByd++y+7u2Hv7V3v6wsxXzS+ySo7/lUIolE5vJ1c3OzpkyZonfffVcXXXRRWn+3qel4KJN/UVGU2qk9VMJSf2dXQjsOx9V4okOxoTmaeF6BBmVHQlF7Mtbrfuzrb7W9/ux/l2SXOrYeatGv//uTPh/3f/78f3h+JiyI73mrpr44tqbjq6++0qpVq/Twww9r6NChkqSOjlNTYXl5eWk/XiKhwLwY6aJ2t0fhjjDXLgW//qxIz2PUrXqDXntvPvjsK/3n25/Z2oliZ32MteXVL/+WYXzdHdu9cu655+q9997TH//4R7W3t6u5uVmLFi3SlClTdP755zv1NACAAKjd36gHXt9ueyeKteU1Fba8ep9joSMvL0+vvPKKDh48qKuuukrXXXed8vPz9cILLzj1FACAAOjsSugPtQeU6kt+ssPX2PLqf45umR0/frxee+01Jx8SAJCmzq6Ep7eEDuTwNba8+hsHvgFAgPS3Y6dJAz18LTur5/oY+ANnrwBAQAy0Y6cpbh++1p+zW+AMZjoAIACc6thpgrUT5evWjl7XdWTq8DU/zAQFGTMdABAA6ayTcFt21qlOralkYieKX2aCgozQAQABMNB1EqZVXRTTS7+4zNhOFLszQVxqySwurwBAALi9TqI/rr90lCYWDz3VqTXDO1EGsmMGziF0AEAApNOx00tM7UTx20xQUHF5BQACgI6dqflxJiiICB0AEBB07OydNROUihdngoKGyysAECB07EzOmglauG5Pr/cJ80yQKYQOAAgYOnYmZ80Ede/TURLN1dxpZaGeCTKF0AEACA1mgtxF6AAAhAozQe5hISkAADCC0AEAAIwgdAAAACMIHQAAwAhCBwAAMILQAQAAjCB0AAAAIwgdAADACEIHAAAwwrMdSSMh7Ehr1Uzt4RLm2qVw10/t1B4UdmuJJBKJRGaHAgAAwOUVAABgCKEDAAAYQegAAABGEDoAAIARhA4AAGAEoQMAABhB6AAAAEYQOgAAgBGEDgAAYIQnQ8fevXtVXV2tSZMmqbKyUgsWLNA333zj9rCMOXz4sB566CFNnjxZlZWVqqmpUX19vdvDMurkyZOaPXu23nrrLbeHklFNTU2qqalRRUWFKisr9eyzz+qHH35we1hGNTc3a8aMGdq8ebPbQzHm888/1z333KPLL79cV155pR577DE1Nze7PSxjNm3apNtuu02XXXaZrrzySj3zzDNqa2tze1hGdXZ26q677tLjjz/u9lCM8lzo6Ojo0P3336/Kykpt3rxZH330kb7++mv9/ve/d3toxjz44IMqKChQbW2tamtrVVhYqJqaGreHZUxdXZ3uvPNO7dy50+2hZNyjjz6qIUOG6OOPP9aaNWu0adMmrVq1yu1hGbNt2zbNnj1bhw4dcnsoxrS1tem+++7TxIkT9be//U3r169XS0uLnnjiCbeHZkRzc7N+9atf6Y477tDWrVu1du1abdmyRX/+85/dHppRf/rTn7R161a3h2Gc50JHTk6OPvzwQz3wwAMaNGiQ4vG4Tp48qeHDh7s9NCPi8bhisZgeeeQRDRkyREOHDlV1dbX279+veDzu9vAybtOmTbr77rt18803a/To0W4PJ6O+/PJLbdmyRQsWLNDgwYM1ZswY1dTU6I033nB7aEasXbtW8+fP15w5c9weilFHjx7V+PHj9eCDDyonJ0fnnnuuZs+erX/84x9uD82I4cOH6+9//7tuueUWRSIRtbS0qL29PTS/46VTv+c+/PBDXXvttW4PxThXTplta2tTQ0ND0p+NGDFCQ4YMkSTdfvvt2rFjh8aOHat7773X5BAzqq/6V65cedZtGzZsUGlpqQoKCkwML6P6qn38+PHauHGjcnNz9dprrxkenVl1dXUqLCxUSUnJ6dvKysp09OhRffvttxo2bJiLo8u8q666SjNnztSgQYNCFTwuvPBCvfLKK2fdtmHDBv3kJz9xaUTm5efnS5KmTp2qhoYGVVRU6JZbbnF5VGY0NTXpd7/7nVasWBGqWU2LK6Fj165dqq6uTvqz5cuXa/r06ZKkVatWqb29XU899ZTuuecevf3228rOzjY51IywW78krV69Wq+++qpeeuklU8PLqHRqD7oTJ05o8ODBZ91m/fm7774LfOgYMWKE20NwXSKR0AsvvKCNGzfq9ddfd3s4xn344YeKx+OaP3++Hn744R5hLGi6urq0YMEC3XPPPRo/frzbw3GFK6GjsrJS+/bt6/N+eXl5ysvL03/8x3/oiiuu0L59+1ReXm5ghJllp/6Ojg4tXrxY77//vl5++WVNnjzZ0Ogyy+5rHwZDhgzRyZMnz7rN+vPQoUPdGBIMam1t1W9/+1vt3r1br7/+ui6++GK3h2Sc9Tt+wYIFuu222xSPxwMxo9ubl19+WTk5ObrrrrvcHoprPLem4/Dhw6qqqtKxY8dO39bR0SFJgX4znqm5uVl33XWXdu7cqTVr1gQmcOBs48aNU0tLixobG0/fdvDgQY0cOVLRaNTFkSHTDh06pJ/97GdqbW3VmjVrQhU4tm/fruuvv/7073Xp1O/4c845p8fMX9C888472rJliyoqKlRRUaH169dr/fr1qqiocHtoxngudJSWlqqwsFCLFy/WiRMn1NzcrEWLFunqq69WaWmp28PLuO+//1733Xef8vPztXr1ao0ZM8btISFDLrjgAk2aNEnPPfecWltbVV9frxUrVujWW291e2jIoHg8rrvvvluXXXaZVq5cGaoFlJJ08cUXq62tTUuXLlVHR4eOHDmiJUuW6NZbb1VOTo7bw8uoDz74QNu3b9fWrVu1detW3XDDDbrhhhtCtYvFlcsrqUQiEa1YsULPPvusqqqqlJOTo+nTp2vu3LluD82IjRs3avfu3crNzdWUKVPO+tl7770X+B0dYbNs2TI9/fTTuuaaa5SVlaWbbropVNujw+itt97S0aNH9de//lUffPDBWT/bsWOHS6MyZ+jQoXrllVf03HPP6corr1Q0GtXMmTP14IMPuj00GBBJJBIJtwcBAACCz3OXVwAAQDAROgAAgBGEDgAAYAShAwAAGEHoAAAARhA6AACAEYQOAABgBKEDAAAYQegAAABGEDoAAIARhA4AAGAEoQMAABjx/wFiHNZyQkvz9QAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0],X[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:14:29.862524Z",
     "start_time": "2023-09-19T12:14:29.616149Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "KMeans(n_clusters=2, n_init=20)",
      "text/html": "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=2, n_init=20)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=2, n_init=20)</pre></div></div></div></div></div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustering = KMeans(n_clusters = 2,n_init=20)\n",
    "clustering.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:14:29.891393Z",
     "start_time": "2023-09-19T12:14:29.861008Z"
    }
   },
   "outputs": [],
   "source": [
    "clusters = clustering.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:14:30.019714Z",
     "start_time": "2023-09-19T12:14:29.886120Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.collections.PathCollection at 0x135c26a00>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1], c=clusters, s=50)\n",
    "\n",
    "centers = clustering.cluster_centers_\n",
    "plt.scatter(centers[:, 0], centers[:, 1],marker = '*', c='red', s=200, alpha=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:14:30.034729Z",
     "start_time": "2023-09-19T12:14:30.018892Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "array([24, 26])"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.bincount(clusters)\n",
    "# 24 obs are classified in cluster 1, and 26 in cluster 2/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using different values of n_init for k  = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:14:30.344413Z",
     "start_time": "2023-09-19T12:14:30.033896Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, 'N_init = 20')"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 2000x800 with 2 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,2,figsize = (20,8))\n",
    "\n",
    "# n_init = 1\n",
    "clustering = KMeans(n_clusters = 3,n_init=1)\n",
    "clustering.fit(X)\n",
    "\n",
    "clusters = clustering.predict(X)\n",
    "\n",
    "ax[0].scatter(X[:, 0], X[:, 1], c=clusters, s=50)\n",
    "\n",
    "centers = clustering.cluster_centers_\n",
    "ax[0].scatter(centers[:, 0], centers[:, 1],marker = '*', c='red', s=200, alpha=1)\n",
    "ax[0].set_title('N_init = 1')\n",
    "\n",
    "# n_init = 20\n",
    "clustering = KMeans(n_clusters = 3,n_init=20)\n",
    "clustering.fit(X)\n",
    "\n",
    "clusters = clustering.predict(X)\n",
    "\n",
    "ax[1].scatter(X[:, 0], X[:, 1], c=clusters, s=50)\n",
    "\n",
    "centers = clustering.cluster_centers_\n",
    "ax[1].scatter(centers[:, 0], centers[:, 1],marker = '*', c='red', s=200, alpha=1)\n",
    "ax[1].set_title('N_init = 20')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from above that almost all of the points are in same cluterts in both of the graphs, except the one we see in the middle right part. <br>\n",
    "It is suggested that we should not go for a very small value of n_init"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10.5.2  Hierarchical Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:14:30.350008Z",
     "start_time": "2023-09-19T12:14:30.342293Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of first feature before scaling -  1.2021804941282384\n",
      "Mean of first feature after scaling -  2.373795604526663e-16\n"
     ]
    }
   ],
   "source": [
    "# before applying hierachical clutering lets scale the data\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "\n",
    "print('Mean of first feature before scaling - ',X[:,0].mean())\n",
    "print('Mean of first feature after scaling - ',X_scaled[:,0].mean())\n",
    "# after scaling, its pretty close to 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:14:30.592613Z",
     "start_time": "2023-09-19T12:14:30.347956Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x700 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dendrogram\n",
    "plt.figure(figsize=(10, 7))  \n",
    "plt.title(\"Dendrograms\")  \n",
    "dend = shc.dendrogram(shc.linkage(X_scaled, method='ward'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using heirachical clustering for clustering points in 2 groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:14:30.846466Z",
     "start_time": "2023-09-19T12:14:30.623026Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.lines.Line2D at 0x13600a1c0>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x700 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))  \n",
    "plt.title(\"Dendrograms\")  \n",
    "dend = shc.dendrogram(shc.linkage(X_scaled, method='ward'))\n",
    "plt.axhline(y = 7,c = 'red',linestyle = '--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:14:30.860202Z",
     "start_time": "2023-09-19T12:14:30.847144Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/py309/lib/python3.9/site-packages/sklearn/cluster/_agglomerative.py:1005: FutureWarning: Attribute `affinity` was deprecated in version 1.2 and will be removed in 1.4. Use `metric` instead\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": "array([1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n       1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0])"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting predictions \n",
    "cluster = AgglomerativeClustering(n_clusters=2, affinity='euclidean', linkage='ward')  \n",
    "# affinity here is the method used for calucating distance, and through linkage we can speify the different types \n",
    "# of linkages\n",
    "cluster.fit_predict(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:14:31.176482Z",
     "start_time": "2023-09-19T12:14:30.864420Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/py309/lib/python3.9/site-packages/sklearn/cluster/_agglomerative.py:1005: FutureWarning: Attribute `affinity` was deprecated in version 1.2 and will be removed in 1.4. Use `metric` instead\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": "array([1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2,\n       1, 1, 1, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0,\n       0, 0, 0, 0, 0, 2])"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x700 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3 CLUTERSabs\n",
    "plt.figure(figsize=(10, 7))  \n",
    "plt.title(\"Dendrograms\")  \n",
    "dend = shc.dendrogram(shc.linkage(X_scaled, method='ward'))\n",
    "plt.axhline(y = 3.4,c = 'red',linestyle = '--')\n",
    "\n",
    "#getting predictions \n",
    "cluster = AgglomerativeClustering(n_clusters=3, affinity='euclidean', linkage='ward')  \n",
    "# affinity here is the method used for calucating distance, and through linkage we can speify the different types \n",
    "# of linkages\n",
    "cluster.fit_predict(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-19T12:14:31.181828Z",
     "start_time": "2023-09-19T12:14:31.173637Z"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
